TidyTuesday or known more commonly as #TidyTuesday is an excellent approach to practice Data Visualization skills using R.
TidyTuesday is a project that was started by Thomas Mock along with the guidance of Jake Kauppe and came into existence by the efforts of the R for Data Science Online Learning Community (R4DS). Every week, on Monday they post a new data set. You can plot the data based on your work and share it along with the code on Twitter using #TidyTuesday.
Purpose of #TidyTuesday
The data is growing and is random. Yes, it can be ‘tamed’ but putting it in the way it looks more cleaner and representable makes it challenging to handle.
The main purpose is to emphasize on summarizing and arranging the data through charts and graphs; interpreting its meaning using ggplot2, dplyr, tidyr and other tools in tidyverse. Tidyverse is a collection of R packages designed specifically for data science. It is the platform that provides tools to handle these datasets. Using Tidyverse, one can practice tidytuesday and work on enhancing skills.
The project was founded to focus on the ways to clean, wrangle, tidy and plot a new dataset which was done every Tuesday.
Emphasis on projects leads to applying basic R skills, getting feedback, exploring other’s work and gaining new ideas and connecting with the great Rstats community!
What binds an enthusiast with TidyTuesday is building skills with real-world data and acknowledging its distinctiveness. The datasets are open and free. One can even submit a dataset as an Issue if one finds it interesting enough, along with a link and an article.
Tips to make data ‘Tidy’
The 3 rules that make a dataset tidy are:
Each variable must have its own column.
Each observation must have its own row.
Each value must have its own cell.
Submission of Data Sets:
Just follow few simple steps stated here and submit data sets in TidyTuesday
GitHub Reference: TidyTuesday